Revolutionizing Data Processing Using Ecosystem SQL ELT

Last Published: Jul 03, 2024 |
Karthikeyan Mani
Karthikeyan Mani

Principal Product Manager

As the landscape of data management shifts beneath our feet, many organizations continuously seek more efficient and effective ways to handle large volumes of data. One approach that has gained significant traction in recent years is the use of Native Structured Query Language (SQL) for transformation. This method leverages the inherent power and capabilities of the modern data management ecosystem to process data. In this blog, we will explore Ecosystem SQL extract, load, transform (ELT) mapping, its advantages and its capabilities.

Decades ago, Informatica supported pushdown optimization capabilities using ODBC Pushdown Optimizer (PDO) and Advanced Pushdown Optimizer (APDO), where the data integration service translates the transformation logic into SQL queries and sends the SQL queries to the database. In April 2024, Informatica launched an ecosystem-based SQL ELT that leverages the native functions of the ecosystem, restricts transformations to those supported by the ecosystem and ensures guaranteed pushdown.

Key Advantages of Ecosystem SQL ELT

Ecosystem SQL ELT offers some key advantages for enterprises like yours, such as:

  • Simplified architecture: Data pipeline flow is automatically converted to Ecosystem SQL ELT and simplifies the data processing architecture.
  • Cost efficiency: By processing the data within the database/ecosystem and using the ecosystem computing engine, you can reduce the egress charges. 
  • Leveraging existing skills: Most ELT professionals are well-versed in constructing data pipelines, which helps to minimize the learning curve and speed up your integration projects.
  • Performance and scalability: Support is available for all the modern and leading data warehouses and native functions of the respective ecosystem. This results in faster and more scalable data transformation. It also improves efficiency by reducing data movement and latency.

When to Use Ecosystem SQL ELT

Ecosystem SQL ELT is particularly useful in scenarios where data processing needs to be efficient, scalable and easily integrated with modern data architecture. At the same time, it needs to be cost-effective and make use of ecosystem computing.

Below are some of the use cases:

  • Data science and analytics: You can efficiently manage and analyze large datasets to derive valuable insights by reading data within the ecosystem and external data lakes (homogeneous/ heterogeneous).
  • Data integration within the same ecosystem: This facilitates the extraction of your data from the source within the same ecosystem and loads it into the central data warehouse, where it is transformed as required for unified analysis.
  • Data warehouse modeling: This is focused on creating a unified data repository to support business intelligence, reports and data analysis. Doing so requires efficient data storage and quick query performance.
  • Machine learning model training: Large language model (LLM) functions available within the ecosystem enhance various applications by providing advanced natural language processing capabilities, leading to improved efficiency and automated tasks.

Ecosystem SQL ELT in the Informatica Intelligent Data Management Cloud

The Informatica Intelligent Data Management Cloud (IDMC) offers a robust suite of cloud-based data integration and data management solutions for native ecosystem-specific SQL ELT. This capability allows you to efficiently handle large volume of data using SQL ELT within your data warehouse environment.

Figure 1: Select Ecosystem SQL ELT mapping designer to create your mapping.

In ecosystem-specific mapping, you can see only the transformations the particular ecosystem supports. This helps guarantee that the transformations used in the pipeline execute on full pushdown. Informatica supports out-of-box native functions that are available in the respective ecosystem.

Figure 2: Out-of-box support for Ecosystem functions.

During the design time, the developer can validate the pipeline and view the SQL ELT query generated during runtime. 

Figure 3: Generate SQL query and validate the pipeline during the design time.

A developer-friendly midstream preview is available to troubleshoot or review the change in data from one transformation to another. 

Figure 4: Troubleshoot pipeline using midstream preview 

Next Steps

Ecosystem SQL ELT is revolutionizing the way organizations handle data integration and transformation. Informatica is using ecosystem-based mapping to leverage the power and capabilities of modern data warehouses. This approach offers significant advantages in terms of performance, scalability, cost-efficiency and flexibility.

To learn more, refer to the user guide document for Ecosystem SQL ELT:

To get started with Snowflake SQL ELT today, visit


First Published: Jun 30, 2024